R basic packages

library(gplots)
library(MASS)
library(pander)
library(magrittr)
library(dplyr)
library(ggplot2)

Group Heatmaps

This was created calculating the accuracy of each subject per stimuli. First stimuli where binarized based in the answer for the Fine structure emotion, with 1 if is correct and 0 when wrong. The total was divided by the total amount of stimuli or each category (nb0…nb64).

Accuracy for both emotions

HAPPY - Group Heatmap

Ordered by row clusterization

SAD - Group Heatmap

Ordered by row clusterization

Happy vs Sad

Stimuli Correlation for Female-Male | Happy-Sad

Lines for all Subjects

Linear Discriminant Analysis

I ran the LDA analysis again using nb0 aand without the age.
\(Group~nb0+nb2+nb4+nb8+nb16+nb32+nb64\)

##     best     high      low     poor 
## 0.203125 0.187500 0.140625 0.250000
Observed vs. Predicted Frequencies
  best high low poor Sum
Predicted best 13 0 3 0 16
Predicted high 0 12 3 0 15
Predicted low 3 2 9 0 14
Predicted poor 0 2 1 16 19
Sum 16 16 16 16 64 
## [1] 0.78125
Proportions
  best high low poor Sum
Predicted best 0.2031 0 0.04688 0 0.25
Predicted high 0 0.1875 0.04688 0 0.2344
Predicted low 0.04688 0.03125 0.1406 0 0.2188
Predicted poor 0 0.03125 0.01562 0.25 0.2969
Sum 0.25 0.25 0.25 0.25 1

type LDA1 LDA2 LDA3
best -1.915 0.5037 0.229

high -0.05947 -0.7754 0.1844

low -0.837 -0.04239 -0.4417

poor 2.812 0.3141 0.02838

Cannonical Discriminant Variables

## 
## Canonical Discriminant Analysis for type:
## 
##    CanRsq Eigenvalue Difference Percent Cumulative
## 1 0.76604   3.274204     3.0194 90.8426     90.843
## 2 0.20304   0.254766     3.0194  7.0685     97.911
## 3 0.07002   0.075292     3.0194  2.0890    100.000
## 
## Class means:
## 
##           Can1      Can2      Can3
## best  1.915297  0.503690 -0.228964
## high  0.059472 -0.775385 -0.184380
## low   0.836966 -0.042391  0.441722
## poor -2.811735  0.314085 -0.028379
## 
##  raw coefficients:
##          Can1    Can2     Can3
## nb0  29.32658 -1.0821  0.51118
## nb2   1.31803 -5.6461  3.58824
## nb4  -2.54738 -4.9806 -1.25982
## nb8  -1.00784  5.1086 13.00824
## nb16  2.78036  6.1064 -6.97118
## nb32 -0.78734 -8.2108 -4.07971
## nb64  0.52252 -6.2285  4.32074
## 
##  std coefficients:
##           Can1      Can2      Can3
## nb0   1.028038 -0.037934  0.017919
## nb2   0.136166 -0.583298  0.370702
## nb4  -0.298609 -0.583838 -0.147678
## nb8  -0.114526  0.580518  1.478197
## nb16  0.378146  0.830504 -0.948125
## nb32 -0.087808 -0.915715 -0.454991
## nb64  0.051767 -0.617073  0.428069
## 
##  structure coefficients:
##          Can1       Can2      Can3
## nb0   0.99251 -0.0058117  0.027993
## nb2   0.21560 -0.2162105  0.464289
## nb4   0.13691 -0.0946593  0.344383
## nb8   0.17109 -0.0384168  0.601644
## nb16  0.17374 -0.0584374  0.096065
## nb32  0.14170 -0.6663518 -0.106417
## nb64 -0.26811 -0.5486065  0.086451
Raw Coefficients
  Can1 Can2 Can3
nb0 29.33 -1.082 0.5112
nb2 1.318 -5.646 3.588
nb4 -2.547 -4.981 -1.26
nb8 -1.008 5.109 13.01
nb16 2.78 6.106 -6.971
nb32 -0.7873 -8.211 -4.08
nb64 0.5225 -6.228 4.321
Standardized Coefficients
  Can1 Can2 Can3
nb0 1.028 -0.03793 0.01792
nb2 0.1362 -0.5833 0.3707
nb4 -0.2986 -0.5838 -0.1477
nb8 -0.1145 0.5805 1.478
nb16 0.3781 0.8305 -0.9481
nb32 -0.08781 -0.9157 -0.455
nb64 0.05177 -0.6171 0.4281
Structure Coefficients
  Can1 Can2 Can3
nb0 0.9925 -0.005812 0.02799
nb2 0.2156 -0.2162 0.4643
nb4 0.1369 -0.09466 0.3444
nb8 0.1711 -0.03842 0.6016
nb16 0.1737 -0.05844 0.09607
nb32 0.1417 -0.6664 -0.1064
nb64 -0.2681 -0.5486 0.08645 

## Vector scale factor set to  7.676664

Linear Mixed Effects Model

I DON’T REMEMBER IF THIS IS CORRECT I WROTE IT ON A PAPER AT THE LAB. I NEED TO CHECK THIS TOMORROW. 

##          Model df       AIC       BIC   logLik   Test  L.Ratio p-value
## fst.lme      1  7 -631.0812 -603.4267 322.5406                        
## fst.lme1     2  9 -693.1272 -657.5715 355.5636 1 vs 2 66.04607  <.0001
## Linear mixed-effects model fit by maximum likelihood
##  Data: d 
##         AIC       BIC   logLik
##   -693.1272 -657.5715 355.5636
## 
## Random effects:
##  Formula: ~fts | id
##  Structure: General positive-definite, Log-Cholesky parametrization
##             StdDev      Corr  
## (Intercept) 0.107042555 (Intr)
## fts         0.002306663 -0.821
## Residual    0.074602319       
## 
## Fixed effects: response ~ fts + type 
##                  Value   Std.Error  DF   t-value p-value
## (Intercept)  0.7618405 0.021252948 319  35.84635  0.0000
## fts         -0.0090226 0.000339917 319 -26.54362  0.0000
## typehigh     0.0466320 0.025392925  60   1.83642  0.0713
## typelow      0.0308292 0.025392925  60   1.21409  0.2295
## typepoor     0.0048213 0.025392925  60   0.18987  0.8501
##  Correlation: 
##          (Intr) fts    typhgh typelw
## fts      -0.535                     
## typehigh -0.597  0.000              
## typelow  -0.597  0.000  0.500       
## typepoor -0.597  0.000  0.500  0.500
## 
## Standardized Within-Group Residuals:
##        Min         Q1        Med         Q3        Max 
## -2.6987840 -0.4721199  0.1043887  0.6175353  2.2029515 
## 
## Number of Observations: 384
## Number of Groups: 64
##          Model df       AIC       BIC   logLik   Test L.Ratio p-value
## fst.lme1     1  9 -693.1272 -657.5715 355.5636                       
## fst.lme2     2  7 -565.7383 -538.0838 289.8691 1 vs 2 131.389  <.0001

ANOVA

Is accuracy in the response given by the class belonging and gender?

mod1 <- aov(accuracy[,1]~factor(cases$class)+factor(cases$gender))
summary(mod1)
##                      Df  Sum Sq Mean Sq F value Pr(>F)    
## factor(cases$class)   3 0.22681 0.07560  60.548 <2e-16 ***
## factor(cases$gender)  1 0.00006 0.00006   0.049  0.826    
## Residuals            59 0.07367 0.00125                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TukeyHSD(mod1)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = accuracy[, 1] ~ factor(cases$class) + factor(cases$gender))
## 
## $`factor(cases$class)`
##                  diff          lwr          upr     p adj
## high-best -0.06250000 -0.095529357 -0.029470643 0.0000315
## low-best  -0.03515625 -0.068185607 -0.002126893 0.0327109
## poor-best -0.16015625 -0.193185607 -0.127126893 0.0000000
## low-high   0.02734375 -0.005685607  0.060373107 0.1383047
## poor-high -0.09765625 -0.130685607 -0.064626893 0.0000000
## poor-low  -0.12500000 -0.158029357 -0.091970643 0.0000000
## 
## $`factor(cases$gender)`
##            diff         lwr        upr     p adj
## M-F 0.001953125 -0.01572368 0.01962993 0.8257844